Peter Nabende


2023

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MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages
Cheikh M. Bamba Dione | David Ifeoluwa Adelani | Peter Nabende | Jesujoba Alabi | Thapelo Sindane | Happy Buzaaba | Shamsuddeen Hassan Muhammad | Chris Chinenye Emezue | Perez Ogayo | Anuoluwapo Aremu | Catherine Gitau | Derguene Mbaye | Jonathan Mukiibi | Blessing Sibanda | Bonaventure F. P. Dossou | Andiswa Bukula | Rooweither Mabuya | Allahsera Auguste Tapo | Edwin Munkoh-Buabeng | Victoire Memdjokam Koagne | Fatoumata Ouoba Kabore | Amelia Taylor | Godson Kalipe | Tebogo Macucwa | Vukosi Marivate | Tajuddeen Gwadabe | Mboning Tchiaze Elvis | Ikechukwu Onyenwe | Gratien Atindogbe | Tolulope Adelani | Idris Akinade | Olanrewaju Samuel | Marien Nahimana | Théogène Musabeyezu | Emile Niyomutabazi | Ester Chimhenga | Kudzai Gotosa | Patrick Mizha | Apelete Agbolo | Seydou Traore | Chinedu Uchechukwu | Aliyu Yusuf | Muhammad Abdullahi | Dietrich Klakow
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.

2022

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MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Adelani | Graham Neubig | Sebastian Ruder | Shruti Rijhwani | Michael Beukman | Chester Palen-Michel | Constantine Lignos | Jesujoba Alabi | Shamsuddeen Muhammad | Peter Nabende | Cheikh M. Bamba Dione | Andiswa Bukula | Rooweither Mabuya | Bonaventure F. P. Dossou | Blessing Sibanda | Happy Buzaaba | Jonathan Mukiibi | Godson Kalipe | Derguene Mbaye | Amelia Taylor | Fatoumata Kabore | Chris Chinenye Emezue | Anuoluwapo Aremu | Perez Ogayo | Catherine Gitau | Edwin Munkoh-Buabeng | Victoire Memdjokam Koagne | Allahsera Auguste Tapo | Tebogo Macucwa | Vukosi Marivate | Mboning Tchiaze Elvis | Tajuddeen Gwadabe | Tosin Adewumi | Orevaoghene Ahia | Joyce Nakatumba-Nabende | Neo Lerato Mokono | Ignatius Ezeani | Chiamaka Chukwuneke | Mofetoluwa Oluwaseun Adeyemi | Gilles Quentin Hacheme | Idris Abdulmumin | Odunayo Ogundepo | Oreen Yousuf | Tatiana Moteu | Dietrich Klakow
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and recently proposed methods like cross-lingual transfer will be effective. In this paper, we aim to move towards solutions for these challenges, focusing on the task of named entity recognition (NER). We present the creation of the largest to-date human-annotated NER dataset for 20 African languages. We study the behaviour of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, empirically demonstrating that the choice of source transfer language significantly affects performance. While much previous work defaults to using English as the source language, our results show that choosing the best transfer language improves zero-shot F1 scores by an average of 14% over 20 languages as compared to using English.

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A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Adelani | Jesujoba Alabi | Angela Fan | Julia Kreutzer | Xiaoyu Shen | Machel Reid | Dana Ruiter | Dietrich Klakow | Peter Nabende | Ernie Chang | Tajuddeen Gwadabe | Freshia Sackey | Bonaventure F. P. Dossou | Chris Emezue | Colin Leong | Michael Beukman | Shamsuddeen Muhammad | Guyo Jarso | Oreen Yousuf | Andre Niyongabo Rubungo | Gilles Hacheme | Eric Peter Wairagala | Muhammad Umair Nasir | Benjamin Ajibade | Tunde Ajayi | Yvonne Gitau | Jade Abbott | Mohamed Ahmed | Millicent Ochieng | Anuoluwapo Aremu | Perez Ogayo | Jonathan Mukiibi | Fatoumata Ouoba Kabore | Godson Kalipe | Derguene Mbaye | Allahsera Auguste Tapo | Victoire Memdjokam Koagne | Edwin Munkoh-Buabeng | Valencia Wagner | Idris Abdulmumin | Ayodele Awokoya | Happy Buzaaba | Blessing Sibanda | Andiswa Bukula | Sam Manthalu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.

2011

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Mining Transliterations from Wikipedia using Dynamic Bayesian Networks
Peter Nabende
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Applying a Dynamic Bayesian Network Framework to Transliteration Identification
Peter Nabende
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Identification of transliterations is aimed at enriching multilingual lexicons and improving performance in various Natural Language Processing (NLP) applications including Cross Language Information Retrieval (CLIR) and Machine Translation (MT). This paper describes work aimed at using the widely applied graphical models approach of ‘Dynamic Bayesian Networks (DBNs) to transliteration identification. The task of estimating transliteration similarity is not very different from specific identification tasks where DBNs have been successfully applied; it is also possible to adapt DBN models from the other identification domains to the transliteration identification domain. In particular, we investigate the applicability of a DBN framework initially proposed by Filali and Bilmes (2005) to learn edit distance estimation parameters for use in pronunciation classification. The DBN framework enables the specification of a variety of models representing different factors that can affect string similarity estimation. Three DBN models associated with two of the DBN classes originally specified by Filali and Bilmes (2005) have been tested on an experimental set up of Russian-English transliteration identification. Two of the DBN models result in high transliteration identification accuracy and combining the models leads to even much better transliteration identification accuracy.

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Mining Transliterations from Wikipedia Using Pair HMMs
Peter Nabende
Proceedings of the 2010 Named Entities Workshop

2009

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Transliteration System Using Pair HMM with Weighted FSTs
Peter Nabende
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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